TY  - JOUR
T1  - Behaviour Analysis Model for Social Networks using Genetic Weighted Fuzzy C-Means Clustering and Neuro-Fuzzy Classifier
AU - Priya, P. Indira AU - Ghosh, D.K. AU - Kannan, A. AU - , S. Ganapathy 
JO  - International Journal of Soft Computing
VL  - 9
IS  - 3
SP  - 138
EP  - 142
PY  - 2014
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2014.138.142
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2014.138.142
KW  - Clustering
KW  -Genetic algorithms
KW  -global optimization
KW  -Weighted Fuzzy C-Means algorithm
KW  -Weighted Fuzzy C-Means algorithm
AB  - Genetic algorithms are helpful to make effective decisions using suitable 
  fitness functions. They can be used to perform both clustering and classification. 
  However, Clustering algorithms enhanced only with genetic operators are not 
  sufficient for making decision in many critical applications. In this study, 
  researchers propose a new user behaviour analysis model by combining Genetic 
  algorithm with Weighted Fuzzy C-Means Clustering Algorithm (GNWFCMA) for effective 
  clustering. The proposed clustering algorithm is used to improve the classification 
  accuracy by providing initial groups. In addition, researchers use a five factor 
  analysis also for effective clustering. Finally, researchers use a neuro-fuzzy 
  classifier for classifying the data. The experimental results obtained from 
  this study shows that the clustering results when combined with classification 
  algorithm provides better classification accuracy when tested with Weblog dataset.
ER  -